Solving dynamic multi-objective optimization problems via quantifying intensity of environment changes and ensemble learning-based prediction strategies

Algorithms designed to solve dynamic multi-objective optimization problems (DMOPs) need to consider all of the multiple conflicting objectives to determine the optimal solutions. However, objective functions, constraints or parameters can change over time, which presents a considerable challenge. Algorithms should be able not only to identify the optimal solution but also to quickly detect and respond to any changes of environment. In order to enhance the capability of detection and response to environmental changes, we propose a dynamic multi-objective optimization (DMOO) algorithm based on the detection of environment change intensity and ensemble learning (DMOO-DECI&EL). First, we propose a method for detecting environmental change intensity, where the change intensity is quantified and used to design response strategies. Second, a series of response strategies under the framework of ensemble learning are given to handle complex environmental changes. Finally, a boundary learning method is introduced to enhance the diversity and uniformity of the solutions. Experimental results on 14 benchmark functions demonstrate that the proposed DMOO-DECI&EL algorithm achieves the best comprehensive performance across three evaluation criteria, which indicates that DMOO-DECI&EL has better robustness and convergence and can generate solutions with better diversity compared to five other state-of-the-art dynamic prediction strategies. In addition, the application of DMOO-DECI&EL to the real-world scenario, namely the economic power dispatch problem, shows that the proposed method can effectively handle real-world DMOPs.